Patients with NSAIDs using Eunomia: an exploratory analysis

Executive summary: NSAID Drug Initiation Analysis

Conducted exploratory analysis on patients initiating NSAIDs using synthetic OMOP CDM v5.4 database. NSAID cohort defined as Cohort_Definition_ID=4.

Key aspects analyzed:

Demographics:

• Age and gender distribution
• Baseline drug use
• Baseline conditions
• Post-index outcomes, particularly GI bleeding (Condition Concept ID 192671)

Methodology:

• Used Quarto for dynamic reporting
• Created visual representations for clarity
• Organized code and documentation in GitHub repository

Analysis provides insights into NSAID initiation patterns and potential outcomes, contributing to healthcare analytics discussions on NSAID safety.

Cohorts created in table main.cohort

  cohortId       name
1        1  Celecoxib
2        2 Diclofenac
3        3    GiBleed
4        4     NSAIDs
                                                                                       description
1    A simplified cohort definition for new users of celecoxib, designed specifically for Eunomia.
2    A simplified cohort definition for new users ofdiclofenac, designed specifically for Eunomia.
3 A simplified cohort definition for gastrointestinal bleeding, designed specifically for Eunomia.
4       A simplified cohort definition for new users of NSAIDs, designed specifically for Eunomia.
  count
1  1844
2   850
3   479
4  2694

Study population

sql <- "SELECT * FROM main.cohort WHERE COHORT_DEFINITION_ID = 4;"

NSAIDs <- querySql(connection = connection, sql = sql) |>
  as.data.frame()

query_demo <- "SELECT person.*, concept.concept_name AS gender 
FROM person 
LEFT JOIN concept AS concept
  ON person.GENDER_CONCEPT_ID=concept.CONCEPT_ID
WHERE person.person_id IN (
    SELECT subject_id
    FROM main.cohort
    WHERE COHORT_DEFINITION_ID = 4
);"

demo <- querySql(connection = connection, sql = query_demo) |>
  as.data.frame()

demo <- demo %>%
  left_join(NSAIDs, by=c("PERSON_ID" = "SUBJECT_ID")) %>%
  mutate(age = as.numeric(as.Date(COHORT_START_DATE)-as.Date(BIRTH_DATETIME))/365.25)

comma <- function(x) format(x, digits = 2, big.mark = ",")

There are 2,694 patients in the cohort who initiated NSAID use.

Table 1. Characteristics of patients using NSAIDs

x <- table1(~age+RACE_SOURCE_VALUE| GENDER, data=demo,extra.col=list(`P-value`=pvalue), extra.col.pos=3)

Warning in chisq.test(table(y, g)): Chi-squared approximation may be incorrect

x %>%
  kbl() %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

	FEMALE	MALE	P-value	Overall
	(N=1373)	(N=1321)		(N=2694)
age
Mean (SD)	38.7 (3.34)	38.6 (3.18)	0.309	38.6 (3.26)
Median [Min, Max]	38.6 [31.0, 46.6]	38.5 [31.5, 46.7]		38.6 [31.0, 46.7]
RACE_SOURCE_VALUE
asian	112 (8.2%)	100 (7.6%)	0.665	212 (7.9%)
black	175 (12.7%)	163 (12.3%)		338 (12.5%)
hispanic	214 (15.6%)	221 (16.7%)		435 (16.1%)
native	6 (0.4%)	8 (0.6%)		14 (0.5%)
other	2 (0.1%)	0 (0%)		2 (0.1%)
white	864 (62.9%)	829 (62.8%)		1693 (62.8%)

Drug use at baseline

query_drug <- "SELECT 
  de.*,
  coh.cohort_start_date,
  c.concept_name AS drug_name
FROM 
  drug_era de
JOIN 
  main.cohort coh
  ON de.person_id = coh.subject_id
LEFT JOIN observation_period AS op
  ON coh.subject_id=op.person_id
LEFT JOIN concept AS c 
  ON de.drug_concept_id=c.concept_id
WHERE 
  coh.cohort_definition_id = 4
  AND de.drug_era_end_date <= coh.cohort_start_date;
"
drug <- querySql(connection = connection, sql = query_drug) |>
  as.data.frame()

drug_n <- drug %>% distinct(PERSON_ID, DRUG_NAME) %>%
  group_by(PERSON_ID) %>%
  summarise(count = n()) 

On average, patients took 9 medications.

Table 2. NSAIDs used in patients

 drug_nsaid <- drug %>% 
  distinct(PERSON_ID, DRUG_NAME) %>%
  filter(DRUG_NAME %in% c("Aspirin", "celecoxib", "Diclofenac", "Ibuprofen", "Naproxen")) %>%
     group_by(DRUG_NAME) %>%
   summarise(COUNT=n()) %>%
   mutate(`PERCENTAGE (%)` = round(COUNT/nrow(NSAIDs)*100,1)) %>%
   arrange(desc(COUNT)) 

 drug_nsaid %>%
   kbl() %>%
   kable_styling(bootstrap_options = c("striped", "hover"))

DRUG_NAME	COUNT	PERCENTAGE (%)
Aspirin	1919	71.2
celecoxib	1844	68.4
Ibuprofen	856	31.8
Diclofenac	850	31.6
Naproxen	329	12.2

Table 3. List of drugs used (excluding NSAIDs)

drug_x_nsaid <- drug %>% distinct(PERSON_ID, DRUG_NAME) %>%
  filter(!DRUG_NAME %in% c("Aspirin", "celecoxib", "Diclofenac", "Ibuprofen", "Naproxen")) %>%
  group_by(DRUG_NAME) %>%
  summarise(COUNT=n()) %>%
  mutate(`PERCENTAGE (%)` = round(COUNT/nrow(NSAIDs)*100,1)) %>%
  arrange(desc(COUNT)) 
drug_x_nsaid %>%
  kbl() %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

DRUG_NAME	COUNT	PERCENTAGE (%)
Acetaminophen	2628	97.6
Amoxicillin	1756	65.2
Clavulanate	1662	61.7
Penicillin V	1532	56.9
Dextromethorphan	1051	39.0
Doxylamine	1051	39.0
Penicillin G	826	30.7
Inert Ingredients	682	25.3
Norethindrone	628	23.3
Mestranol	605	22.5
Doxycycline	449	16.7
Ampicillin	425	15.8
Nitrofurantoin	379	14.1
Phenazopyridine	379	14.1
Ethinyl Estradiol	274	10.2
norgestimate	255	9.5
Meperidine	214	7.9
Oxycodone	199	7.4
Hydrocodone	193	7.2
fluticasone	148	5.5
Cefaclor	121	4.5
Prednisone	118	4.4
Diphenhydramine	105	3.9
Hydrocortisone	105	3.9
Albuterol	101	3.7
Epinephrine	100	3.7
Chlorpheniramine	75	2.8
Cefuroxime	74	2.7
Atropine	72	2.7
Methylphenidate	66	2.4
Ferrous fumarate	62	2.3
Amiodarone	60	2.2
Medroxyprogesterone	53	2.0
Diazepam	51	1.9
salmeterol	48	1.8
Simvastatin	41	1.5
Nitroglycerin	40	1.5
Carbamazepine	39	1.4
Levonorgestrel	34	1.3
Amlodipine	33	1.2
clopidogrel	31	1.2
Astemizole	26	1.0
Morphine	24	0.9
Ondansetron	24	0.9
Piperacillin	24	0.9
Propofol	24	0.9
Rocuronium	24	0.9
Sodium Chloride	24	0.9
heparin	24	0.9
tazobactam	24	0.9
Midazolam	23	0.9
Isoflurane	17	0.6
alteplase	16	0.6
remifentanil	12	0.4
Vitamin B 12	10	0.4
fexofenadine	10	0.4
Etonogestrel	8	0.3
Loratadine	8	0.3
Cyclosporine	7	0.3
Terfenadine	7	0.3
desflurane	5	0.2
drospirenone	5	0.2
Alfentanil	4	0.1
Fentanyl	4	0.1
Sufentanil	4	0.1
Cetirizine	3	0.1
ferrous sulfate	3	0.1
norelgestromin	3	0.1
Methotrexate	2	0.1
atorvastatin	2	0.1
sevoflurane	2	0.1
Lorazepam	1	0.0

Baseline conditions

Allergies

query_obs<- "SELECT 
  obs.*,
  coh.cohort_start_date,
  obs_concept_syn.concept_synonym_name AS obs_name
FROM 
  observation obs
JOIN main.cohort coh
  ON obs.person_id = coh.subject_id
LEFT JOIN concept_synonym AS obs_concept_syn
  ON obs.OBSERVATION_CONCEPT_ID=obs_concept_syn.concept_id
WHERE 
  coh.cohort_definition_id = 4
  AND obs.observation_date <= coh.cohort_start_date;
"
obs <- DatabaseConnector::querySql(connection = connection, sql = query_obs) |>
  as.data.frame()
obs_n <- obs %>% distinct(PERSON_ID, OBS_NAME) %>%
  group_by(PERSON_ID) %>%
  summarise(count = n()) 
obs_10 <- obs %>% distinct(PERSON_ID, OBS_NAME) %>%
  mutate(OBS_NAME = gsub("\\s*\\(.*?\\)", "", OBS_NAME)) %>% # REMOVE TEXT IN PARENTHESIS
  group_by(OBS_NAME) %>%
  summarise(COUNT=n()) %>%
  mutate(`PERCENTAGE (%)` = round(COUNT/nrow(NSAIDs)*100,1)) %>%
  arrange(desc(COUNT)) 

There were 525 patients in the OBSERVATION dataset. Below is a table of their previous allergies. No patients were allergic to NSAIDs.

Table 4. Allergies of patients

obs_10 %>%
  kbl() %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

OBS_NAME	COUNT	PERCENTAGE (%)
Allergy to mold	304	11.3
Allergy to animal dander	298	11.1
History of appendectomy	244	9.1
Allergy to grass pollen	220	8.2
Allergy to tree pollen	216	8.0
Allergy to house dust mite	206	7.6
Shellfish allergy	186	6.9
Allergy to mildew	152	5.6
Allergy to mould	152	5.6
Animal dander allergy	149	5.5
Dander allergy	149	5.5
History of cardiac arrest	144	5.3
History of single seizure	134	5.0
History of appendicectomy	122	4.5
Allergy to hay	110	4.1
House dust mite allergy	103	3.8
Allergy to peanuts	100	3.7
Allergy to bee venom	84	3.1
Latex allergy	80	3.0
Allergy to wheat	76	2.8
Allergy to eggs	74	2.7
Allergy to fish	72	2.7
History of one seizure ever	67	2.5
Allergy to nut	66	2.4
Allergy to dairy product	64	2.4
Nut allergy	33	1.2
Allergy to soy	22	0.8
Allergy to soya	11	0.4
History of myocardial infarction	10	0.4
Past history of myocardial infarction	5	0.2
Attempted suicide - cut/stab	2	0.1

Conditions at baseline

query_condition <- "SELECT 
  ce.*,
  coh.cohort_start_date,
  condition_concept.concept_name AS condition_name,
  condition_concept.domain_id AS domain
FROM 
  condition_era ce
JOIN main.cohort coh
  ON ce.person_id = coh.subject_id
LEFT JOIN observation_period AS op
  ON coh.subject_id=op.person_id
LEFT JOIN concept AS condition_concept 
  ON ce.CONDITION_CONCEPT_ID=condition_concept.concept_id
WHERE 
  coh.cohort_definition_id = 4
  AND ce.condition_era_start_date <= coh.cohort_start_date;
"

condition <- DatabaseConnector::querySql(connection = connection, sql = query_condition) |>
  as.data.frame()

condition_n <- condition %>% distinct(PERSON_ID, CONDITION_NAME) %>%
  group_by(PERSON_ID) %>%
  summarise(count = n()) 

There were 73 unique conditions for patients using NSAIDs in the CONDITION_ERA datasets. On average, people had 10 conditions.

Table 5. Conditions of patients using NSAIDs at baseline

 condition_num <- condition %>% distinct(PERSON_ID, CONDITION_NAME) %>%
  group_by(CONDITION_NAME) %>%
  summarise(COUNT=n()) %>%
  mutate(`PERCENTAGE (%)` = round(COUNT/nrow(NSAIDs)*100,1)) %>%
  arrange(desc(COUNT)) 
 condition_num%>%
  kbl() %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

CONDITION_NAME	COUNT	PERCENTAGE (%)
Osteoarthritis	2694	100.0
Viral sinusitis	2658	98.7
Acute viral pharyngitis	2463	91.4
Acute bronchitis	2346	87.1
Otitis media	1979	73.5
Streptococcal sore throat	1431	53.1
Sprain of ankle	1074	39.9
Peptic ulcer	802	29.8
Concussion with no loss of consciousness	642	23.8
Sinusitis	583	21.6
Chronic sinusitis	571	21.2
Acute bacterial sinusitis	535	19.9
Sprain of wrist	524	19.5
Whiplash injury to neck	485	18.0
Ulcerative colitis	413	15.3
Esophagitis	409	15.2
Diverticular disease	405	15.0
Angiodysplasia of stomach	388	14.4
Polyp of colon	380	14.1
Fracture of forearm	372	13.8
Fracture of clavicle	333	12.4
Fracture subluxation of wrist	331	12.3
Laceration of foot	327	12.1
Facial laceration	314	11.7
Laceration of forearm	313	11.6
Fracture of ankle	311	11.5
Laceration of thigh	307	11.4
Laceration of hand	302	11.2
No matching concept	258	9.6
Child attention deficit disorder	253	9.4
Escherichia coli urinary tract infection	242	9.0
First degree burn	236	8.8
Concussion with loss of consciousness	178	6.6
Cystitis	178	6.6
Fracture of rib	177	6.6
Injury of tendon of the rotator cuff of shoulder	163	6.1
Second degree burn	140	5.2
Appendicitis	122	4.5
Acute allergic reaction	115	4.3
Contact dermatitis	113	4.2
Childhood asthma	96	3.6
Perennial allergic rhinitis with seasonal variation	79	2.9
Cardiac arrest	72	2.7
Closed fracture of hip	69	2.6
Seizure disorder	67	2.5
Injury of medial collateral ligament of knee	66	2.4
Perennial allergic rhinitis	64	2.4
Anemia	63	2.3
Coronary arteriosclerosis	59	2.2
Atopic dermatitis	54	2.0
Injury of anterior cruciate ligament	54	2.0
Tear of meniscus of knee	54	2.0
Concussion injury of brain	52	1.9
Rupture of patellar tendon	44	1.6
Pulmonary emphysema	35	1.3
Rupture of appendix	31	1.2
Bullet wound	27	1.0
Seasonal allergic rhinitis	25	0.9
Acute cholecystitis	24	0.9
Cerebrovascular accident	24	0.9
Emphysematous bronchitis	24	0.9
Epilepsy	24	0.9
Gallstone	24	0.9
Recurrent urinary tract infection	20	0.7
Traumatic brain injury	20	0.7
Third degree burn	14	0.5
Fracture of vertebral column without spinal cord injury	12	0.4
Rheumatoid arthritis	6	0.2
Asthma	5	0.2
Myocardial infarction	5	0.2
Pyelonephritis	4	0.1
Chronic paralysis due to lesion of spinal cord	3	0.1
Fracture of vertebral column with spinal cord injury	3	0.1

Compare conditions by gender

condition_gender <- condition %>% 
  distinct(PERSON_ID, CONDITION_NAME) %>%
  left_join(demo) %>%
  group_by(CONDITION_NAME, GENDER) %>%
  summarise(COUNT=n()) %>%
  mutate(PERCENTAGE = if_else(GENDER == "FEMALE", 100*COUNT/nrow(demo[demo$GENDER == "FEMALE",]), 100*COUNT/nrow(demo[demo$GENDER=="MALE",])))

condition_gender_select <- SharedData$new(condition_gender)

Select conditions:

condition_gender_select %>% plot_ly(
  x = ~ CONDITION_NAME,
  y = ~ PERCENTAGE, 
  color = ~GENDER, 
  type = "bar") %>%
  layout(
    xaxis = list(title = "", categoryorder = "trace"))

Post-index

Table 6. Conditions post-index in patients using NSAIDs

query_condition_post <- "SELECT
  ce.*,
  coh.cohort_start_date,
  condition_concept.concept_name AS condition_name,
  condition_concept.domain_id AS domain
FROM 
  condition_era ce
JOIN main.cohort coh
  ON ce.person_id = coh.subject_id
LEFT JOIN observation_period AS op
  ON coh.subject_id=op.person_id
LEFT JOIN concept AS condition_concept 
  ON ce.CONDITION_CONCEPT_ID=condition_concept.concept_id
WHERE 
  coh.cohort_definition_id = 4
  AND ce.condition_era_start_date > coh.cohort_start_date;
"

condition_post <- DatabaseConnector::querySql(connection = connection, sql = query_condition_post) |>
  as.data.frame()

condition_post_n <- condition_post %>% distinct(PERSON_ID, CONDITION_NAME) %>%
  group_by(PERSON_ID) %>%
  summarise(count = n()) 

query_condition_ob <- "SELECT
  co.*,
  coh.cohort_start_date,
  condition_concept.concept_name AS condition_name,
  condition_concept.domain_id AS domain
FROM 
  CONDITION_OCCURRENCE co
JOIN main.cohort coh
  ON co.person_id = coh.subject_id
LEFT JOIN observation_period AS op
  ON coh.subject_id=op.person_id
LEFT JOIN concept AS condition_concept 
  ON co.CONDITION_CONCEPT_ID=condition_concept.concept_id
WHERE 
  coh.cohort_definition_id = 4
  AND co.condition_start_date <= coh.cohort_start_date;
"

condition_ob <- DatabaseConnector::querySql(connection = connection, sql = query_condition_ob) |>
  as.data.frame()

 condition_post_10 <- condition_post %>% distinct(PERSON_ID, CONDITION_NAME) %>%
  group_by(CONDITION_NAME) %>%
  summarise(COUNT=n()) %>%
  mutate(`PERCENTAGE (%)` = round(COUNT/nrow(NSAIDs)*100,1)) %>%
  arrange(desc(COUNT)) 
 condition_post_10 %>%
  kbl() %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

CONDITION_NAME	COUNT	PERCENTAGE (%)
Viral sinusitis	2066	76.7
Acute viral pharyngitis	1664	61.8
Acute bronchitis	1515	56.2
Streptococcal sore throat	536	19.9
Gastrointestinal hemorrhage	479	17.8
Sprain of ankle	478	17.7
Sinusitis	315	11.7
Acute bacterial sinusitis	312	11.6
Concussion with no loss of consciousness	282	10.5
Whiplash injury to neck	267	9.9
Chronic sinusitis	251	9.3
Cerebrovascular accident	200	7.4
Sprain of wrist	188	7.0
Escherichia coli urinary tract infection	183	6.8
Laceration of hand	177	6.6
Osteoporosis	171	6.3
No matching concept	169	6.3
Laceration of thigh	167	6.2
Laceration of forearm	164	6.1
Fracture of forearm	161	6.0
Facial laceration	158	5.9
Otitis media	154	5.7
Atrial fibrillation	137	5.1
First degree burn	137	5.1
Laceration of foot	135	5.0
Coronary arteriosclerosis	132	4.9
Fracture subluxation of wrist	129	4.8
Fracture of ankle	128	4.8
Fracture of clavicle	124	4.6
Alzheimer's disease	117	4.3
Cystitis	115	4.3
Injury of tendon of the rotator cuff of shoulder	93	3.5
Second degree burn	81	3.0
Concussion with loss of consciousness	80	3.0
Fracture of rib	77	2.9
Cardiac arrest	66	2.4
Closed fracture of hip	65	2.4
Myocardial infarction	62	2.3
Pathological fracture due to osteoporosis	56	2.1
Pneumonia	52	1.9
Injury of medial collateral ligament of knee	43	1.6
Pulmonary emphysema	42	1.6
Anemia	39	1.4
Appendicitis	35	1.3
Rupture of patellar tendon	35	1.3
Hypothyroidism	31	1.2
Tear of meniscus of knee	28	1.0
Injury of anterior cruciate ligament	26	1.0
Emphysematous bronchitis	24	0.9
Bullet wound	19	0.7
Familial Alzheimer's disease of early onset	19	0.7
Concussion injury of brain	18	0.7
Recurrent urinary tract infection	13	0.5
Rheumatoid arthritis	12	0.4
Seizure disorder	12	0.4
Acute cholecystitis	11	0.4
Fracture of vertebral column without spinal cord injury	11	0.4
Gallstone	11	0.4
Rupture of appendix	10	0.4
Third degree burn	10	0.4
Traumatic brain injury	7	0.3
Epilepsy	6	0.2
Pyelonephritis	5	0.2
Acute allergic reaction	2	0.1

condition_all <- condition %>% distinct(PERSON_ID, CONDITION_NAME) %>%
  group_by(CONDITION_NAME) %>%
  summarise(COUNT_PRE=n()) 
condition_post_all <- condition_post %>% distinct(PERSON_ID, CONDITION_NAME) %>%
  group_by(CONDITION_NAME) %>%
  summarise(COUNT_POST=n()) 
condition_x <- condition_all %>% 
  full_join(condition_post_all) %>%
  mutate_all(tidyr::replace_na,0) %>%
  tidyr::pivot_longer(!CONDITION_NAME, names_to = "time", values_to = "COUNT") %>%
  mutate(TIME = if_else(time == "COUNT_PRE", 1, 2))

condition_all_select <- SharedData$new(condition_x)

Compare conditions pre- and post- index:

filter_select("CONDITION_NAME", "Select conditions:", condition_all_select, ~ CONDITION_NAME)

Select conditions:

function filter_default() {
    document.getElementById("CONDITION_NAME").getElementsByClassName("selectized") 
[0].selectize.setValue("Gastrointestinal hemorrhage", false);
    document.getElementById("CONDITION_GENDER").getElementsByClassName("selectized") 
[0].selectize.setValue("Acute bronchitis", false);
 }
$(document).ready(filter_default);

condition_all_select %>% plot_ly(
  x = ~ TIME,
  y = ~ COUNT, 
  color = ~CONDITION_NAME, 
  type = "scatter", 
  mode = "markers+lines") %>%
  layout(xaxis = list(
    title = "", 
    ticktext = list("Pre-index", "Post-index"), 
    tickvals = list(1,2), 
    tickmode = "array"))

Patients with GI Bleed

GI <- condition_post %>% 
  filter(CONDITION_CONCEPT_ID==192671) %>%
  left_join(demo)

The incidence of GI bleeding increased from 0% to 18% following NSAID initiation.

Table 7. Conditions that have increased post-NSAID use.

condition_increase <- condition_all %>% 
  full_join(condition_post_all) %>%
  mutate_all(tidyr::replace_na,0) %>%
  filter(COUNT_PRE < COUNT_POST) %>%
  mutate(PERCENTAGE_INCREASE = round((COUNT_POST/nrow(demo) - COUNT_PRE/nrow(demo))*100,1)) %>%
  arrange(CONDITION_NAME) 

condition_increase %>%
  kbl() %>%
  kable_styling(bootstrap_options = c("striped", "hover")) 

CONDITION_NAME	COUNT_PRE	COUNT_POST	PERCENTAGE_INCREASE
Alzheimer's disease	0	117	4.3
Atrial fibrillation	0	137	5.1
Cerebrovascular accident	24	200	6.5
Coronary arteriosclerosis	59	132	2.7
Familial Alzheimer's disease of early onset	0	19	0.7
Gastrointestinal hemorrhage	0	479	17.8
Hypothyroidism	0	31	1.2
Myocardial infarction	5	62	2.1
Osteoporosis	0	171	6.3
Pathological fracture due to osteoporosis	0	56	2.1
Pneumonia	0	52	1.9
Pulmonary emphysema	35	42	0.3
Pyelonephritis	4	5	0.0
Rheumatoid arthritis	6	12	0.2

Future analysis

Our analysis has provided valuable insights into NSAID initiation and its correlation with GI bleeding. However, to gain a more comprehensive understanding, I propose further investigation into the following areas:

Drug Strength and Duration of Use: Analyze the impact of NSAID dosage and treatment length on GI bleeding risk, including time-to-event analysis.

Adherence to Treatment: Evaluate patient compliance using pharmacy refill data and its relationship to adverse events.

Specific NSAID types: Compare the risk profiles of different NSAID types (e.g., ibuprofen, naproxen, diclofenac) to identify if certain drugs carry higher risks.

Healthcare utilization patterns: Examine how factors like frequency of doctor visits or hospitalizations correlate with NSAID use and GI bleeding events.

Long-term outcomes: Conduct a longitudinal study to assess long-term consequences of NSAID use and GI bleeding events on patient health and quality of life.

These additional analyses will provide a deeper understanding of NSAID-related risks and inform strategies to enhance patient safety and optimize prescribing practices.